Artificial intelligence generally relates to the study and design of computer systems that exhibit characteristics associated with intelligence, such as, for example, language comprehension, problem solving, pattern recognition, learning, reasoning from incomplete and uncertain information, etc. Artificial intelligence can be achieved by modeling, for example, computer systems with an artificial neural network technology. The full potential of artificial neural networks, however, remains unrealized because of inherent limitations in current implementations.
Neural learning systems can be utilized to process and transfer knowledge more efficiently and effectively, which significantly reduces learning time while improving memory retention. Such artificial neural networks can be useful in applications such as, for example, speech synthesis, diagnostic problems, medicine, business and finance, robotic control, signal processing, computer vision and so forth. Such neural models achieve a human-like performance over more traditional artificial intelligence techniques for some application areas.
Neural networks can be taught by a successive presentation of sets of signals to one or more primary inputs with each signal set derived from a pattern belonging to a class of patterns, all having some common features or characteristics. Each time a set of signals is presented to the primary inputs, a synaptic weight must be adapted for the neural network to learn from the input. Such neural networks must be first trained with learning or training data before they are capable of generalizing. Acquiring such training data is time-consuming and expensive.
Examples of neural and synaptic learning systems are disclosed in the following issued United States patents, which are incorporated herein by reference and indicated respectively by patent numbers and titles:    U.S. Pat. No. 7,426,501 Nanotechnology neural network methods and systems    U.S. Pat. No. 7,420,396 Universal logic gate utilizing nanotechnology    U.S. Pat. No. 7,412,428 Application of hebbian and anti-hebbian learning to nanotechnology-based physical neural networks    U.S. Pat. No. 7,409,375 Plasticity-induced self organizing nanotechnology for the extraction of independent components from a data stream    U.S. Pat. No. 7,398,259 Training of a physical neural network    U.S. Pat. No. 7,392,230 Physical neural network liquid state machine utilizing nanotechnology    U.S. Pat. No. 7,107,252 Pattern recognition utilizing a nanotechnology-based neural network    U.S. Pat. No. 7,039,619 Utilized nanotechnology apparatus using a neutral network, a solution and a connection gap    U.S. Pat. No. 7,028,017 Temporal summation device utilizing nanotechnology    U.S. Pat. No. 6,995,649 Variable resistor apparatus formed utilizing nanotechnology    U.S. Pat. No. 6,889,216 Physical neural network design incorporating nanotechnology
Examples of neural and synaptic learning systems are also disclosed in the following United States patent application publications, which are also incorporated herein by reference and indicated respectively by patent numbers and titles:    20080258773 Universal Logic gate utilizing nanotechnology    20070176643 Universal logic gate utilizing nanotechnology    20070022064 Methodology for the configuration and repair of unreliable switching elements    20070005532 Plasticity-induced self organizing nanotechnology for the extraction of independent components from a data stream    20060184466 Fractal memory and computational methods and systems based on nanotechnology    20060036559 Training of a physical neural network    20050256816 Solution-based apparatus of an artificial neural network formed utilizing nanotechnology    20050151615 Variable resistor apparatus formed utilizing nanotechnology    20050149465 Temporal summation device utilizing nanotechnology    20050149464 Pattern recognition utilizing a nanotechnology-based neural network    20050015351 Nanotechnology neural network methods and systems    20040193558 Adaptive neural network utilizing nanotechnology-based components    20040162796 Application of Hebbian and anti-Hebbian learning to nanotechnology-based physical neural networks    20040153426 Physical neural network liquid state machine utilizing nanotechnology    20040039717 High-density synapse chip using nanoparticles    20030236760 Multi-layer training in a physical neural network formed utilizing nanotechnology    20030177450 Physical neural network design incorporating nanotechnology
Many prior art neural network systems, other than those disclosed above, find it difficult to effectively make choices in a complex world. Also, such neural network systems are unable to associate prior circumstances and actions with the consequences of actions taken over time. Consequently, such systems are unable to provide mechanism that illustrates how successful behaviors are actively explored and learned. Such neural network systems have yet to be extended with an emotional subsystem for handling much more complex situations.
Based on the foregoing it is believed that a need exists for an improved distributed, fine-grained neural learning system. A need also exists for an improved emotional memory control system for generating successful behaviors, as described in greater detail herein.